Screenshot Beautifier

Screenshot Beautifier composites plain screenshots onto styled backdrops using the Canvas 2D API. The original screenshot is decoded into an HTMLImageElement, drawn into a larger output Canvas with configurable padding, given rounded corners through Canvas's roundRect path method, and stamped with a drop shadow drawn via shadowBlur, shadowColor, and shadowOffsetX/Y before the screenshot is rendered.

Backdrops are flexible. Solid colours fill the Canvas with a single fillRect call. Linear and radial gradients are built with createLinearGradient and createRadialGradient using user-tunable colour stops. Mesh-style gradients are approximated by stacking two or three radial gradients with different centres and blend modes, which produces the soft Instagram-style backdrops popular for product launch screenshots.

Padding, corner radius, and shadow strength are exposed as sliders. Their values are stored as percentages of the source's smaller dimension so the relative look stays consistent whether you upload an iPhone screenshot or a 4K desktop capture. There is no fixed pixel value baked in; the same settings produce visually equivalent results across every input size.

An optional device frame layer overlays a phone or browser chrome bezel around the screenshot. Frames are stored as separate PNG assets bundled with the tool; the screenshot is positioned and scaled to fit the frame's content rectangle before the frame is composited on top. This is the look used by Apple in their App Store screenshots and by most landing pages for SaaS products.

Batch processing is supported by queueing multiple screenshots through the same beautifier pipeline with one set of settings. Outputs can be downloaded individually or as a ZIP. Each beautified file is encoded once with Canvas toBlob in PNG, which keeps text crisp and avoids JPEG compression artefacts on UI elements with hard edges.

There is no AI involved. The tool uses deterministic Canvas operations, which means the output is reproducible: the same input plus the same slider values always produces the same output, byte-for-byte. That predictability matters when you need consistent screenshot sets across documentation pages.

Everything runs locally — the input screenshots, the Canvas compositing, and the export. No image bytes are uploaded, which is helpful because screenshots often contain confidential UI from internal dashboards, beta products, or customer accounts.